
Random Forest Classifier (RFC): RFC is an ensemble tree-based learning algorithm [1]}. It aggregates the votes from different decision trees to decide the output class of the instance. RFC can run efficiently on large datasets, handle thousands of input variables, measure the relative importance of each feature, and produces a highly accurate classifier.

Support Vector Machine (SVM): SVM produces high accuracy with less computation power and is widely used in classification tasks. To classify the instances, the SVM finds a hyperplane in \(N\) -dimensional space, where \(N\)  represents the number of features [2]}. The goal of SVM is to perform classification by finding the hyperplane separating the two classes more accurately (maximising the margin between two classes).

